Overview

Dataset statistics

Number of variables15
Number of observations240
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory66.9 KiB
Average record size in memory285.2 B

Variable types

NUM12
CAT3

Warnings

POSS is highly correlated with MINHigh correlation
MIN is highly correlated with POSSHigh correlation
%PFD is highly correlated with %FTAHigh correlation
%FTA is highly correlated with %PFDHigh correlation
%PTS is highly correlated with %FGAHigh correlation
%FGA is highly correlated with %PTSHigh correlation
TEAM is uniformly distributed Uniform
PLAYER has unique values Unique
%3PA has 3 (1.2%) zeros Zeros

Reproduction

Analysis started2022-04-10 17:28:50.086156
Analysis finished2022-04-10 17:28:59.837435
Duration9.75 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

PLAYER
Categorical

UNIQUE

Distinct240
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
Bruce Brown
 
1
Cam Thomas
 
1
Danny Green
 
1
Carmelo Anthony
 
1
Isaiah Stewart
 
1
Other values (235)
235 
ValueCountFrequency (%) 
Bruce Brown10.4%
 
Cam Thomas10.4%
 
Danny Green10.4%
 
Carmelo Anthony10.4%
 
Isaiah Stewart10.4%
 
Lauri Markkanen10.4%
 
Bojan Bogdanovic10.4%
 
Amir Coffey10.4%
 
Tyus Jones10.4%
 
Reggie Jackson10.4%
 
Corey Kispert10.4%
 
Jaren Jackson Jr.10.4%
 
Jrue Holiday10.4%
 
Miles Bridges10.4%
 
Domantas Sabonis10.4%
 
Tim Hardaway Jr.10.4%
 
Talen Horton-Tucker10.4%
 
Jae Crowder10.4%
 
Damion Lee10.4%
 
Raul Neto10.4%
 
Gary Payton II10.4%
 
Cole Anthony10.4%
 
Oshae Brissett10.4%
 
Coby White10.4%
 
Joe Ingles10.4%
 
Other values (215)21589.6%
 
2022-04-10T12:28:59.867731image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique240 ?
Unique (%)100.0%
2022-04-10T12:28:59.932686image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length24
Median length13
Mean length13.175
Min length8

Overview of Unicode Properties

Unique unicode characters54
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e2879.1%
 
2548.0%
 
a2377.5%
 
r2327.3%
 
n2327.3%
 
o2036.4%
 
i1735.5%
 
l1444.6%
 
s1324.2%
 
t1083.4%
 
u812.6%
 
d762.4%
 
y762.4%
 
J581.8%
 
c561.8%
 
m561.8%
 
h541.7%
 
B441.4%
 
M441.4%
 
D421.3%
 
k411.3%
 
A361.1%
 
g361.1%
 
C361.1%
 
v300.9%
 
Other values (29)39412.5%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter234774.2%
 
Uppercase Letter53116.8%
 
Space Separator2548.0%
 
Other Punctuation230.7%
 
Dash Punctuation70.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
J5810.9%
 
B448.3%
 
M448.3%
 
D427.9%
 
A366.8%
 
C366.8%
 
G275.1%
 
R264.9%
 
T264.9%
 
H254.7%
 
K234.3%
 
P214.0%
 
L203.8%
 
S193.6%
 
W173.2%
 
I163.0%
 
N122.3%
 
V101.9%
 
O91.7%
 
E81.5%
 
F71.3%
 
Z30.6%
 
Q10.2%
 
Y10.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e28712.2%
 
a23710.1%
 
r2329.9%
 
n2329.9%
 
o2038.6%
 
i1737.4%
 
l1446.1%
 
s1325.6%
 
t1084.6%
 
u813.5%
 
d763.2%
 
y763.2%
 
c562.4%
 
m562.4%
 
h542.3%
 
k411.7%
 
g361.5%
 
v301.3%
 
b301.3%
 
w170.7%
 
p120.5%
 
z120.5%
 
f90.4%
 
x80.3%
 
j40.2%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
254100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-7100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.1669.6%
 
'730.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin287891.0%
 
Common2849.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e28710.0%
 
a2378.2%
 
r2328.1%
 
n2328.1%
 
o2037.1%
 
i1736.0%
 
l1445.0%
 
s1324.6%
 
t1083.8%
 
u812.8%
 
d762.6%
 
y762.6%
 
J582.0%
 
c561.9%
 
m561.9%
 
h541.9%
 
B441.5%
 
M441.5%
 
D421.5%
 
k411.4%
 
A361.3%
 
g361.3%
 
C361.3%
 
v301.0%
 
b301.0%
 
Other values (25)33411.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
25489.4%
 
.165.6%
 
-72.5%
 
'72.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3162100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e2879.1%
 
2548.0%
 
a2377.5%
 
r2327.3%
 
n2327.3%
 
o2036.4%
 
i1735.5%
 
l1444.6%
 
s1324.2%
 
t1083.4%
 
u812.6%
 
d762.4%
 
y762.4%
 
J581.8%
 
c561.8%
 
m561.8%
 
h541.7%
 
B441.4%
 
M441.4%
 
D421.3%
 
k411.3%
 
A361.1%
 
g361.1%
 
C361.1%
 
v300.9%
 
Other values (29)39412.5%
 

H
Real number (ℝ≥0)

Distinct15
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.2125
Minimum72
Maximum87
Zeros0
Zeros (%)0.0%
Memory size2.0 KiB
2022-04-10T12:28:59.991421image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum72
5-th percentile73
Q176
median78
Q380.25
95-th percentile83
Maximum87
Range15
Interquartile range (IQR)4.25

Descriptive statistics

Standard deviation3.095524168
Coefficient of variation (CV)0.03957838156
Kurtosis-0.5694845756
Mean78.2125
Median Absolute Deviation (MAD)2
Skewness0.1116214457
Sum18771
Variance9.582269874
MonotocityNot monotonic
2022-04-10T12:29:00.044175image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
773514.6%
 
792711.2%
 
762510.4%
 
782410.0%
 
80229.2%
 
81218.8%
 
75187.5%
 
83166.7%
 
82145.8%
 
73135.4%
 
74114.6%
 
8462.5%
 
7252.1%
 
8520.8%
 
8710.4%
 
ValueCountFrequency (%) 
7252.1%
 
73135.4%
 
74114.6%
 
75187.5%
 
762510.4%
 
773514.6%
 
782410.0%
 
792711.2%
 
80229.2%
 
81218.8%
 
ValueCountFrequency (%) 
8710.4%
 
8520.8%
 
8462.5%
 
83166.7%
 
82145.8%
 
81218.8%
 
80229.2%
 
792711.2%
 
782410.0%
 
773514.6%
 

POS
Categorical

Distinct5
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
G
98 
F
62 
G-F
35 
F-C
29 
C
16 
ValueCountFrequency (%) 
G9840.8%
 
F6225.8%
 
G-F3514.6%
 
F-C2912.1%
 
C166.7%
 
2022-04-10T12:29:00.106849image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-04-10T12:29:00.148562image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:29:00.200041image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.533333333
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
G13336.1%
 
F12634.2%
 
-6417.4%
 
C4512.2%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter30482.6%
 
Dash Punctuation6417.4%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
G13343.8%
 
F12641.4%
 
C4514.8%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-64100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin30482.6%
 
Common6417.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
G13343.8%
 
F12641.4%
 
C4514.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
-64100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII368100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
G13336.1%
 
F12634.2%
 
-6417.4%
 
C4512.2%
 

TEAM
Categorical

UNIFORM

Distinct30
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
MIN
 
8
MIA
 
8
OKC
 
8
PHX
 
8
WAS
 
8
Other values (25)
200 
ValueCountFrequency (%) 
MIN83.3%
 
MIA83.3%
 
OKC83.3%
 
PHX83.3%
 
WAS83.3%
 
CLE83.3%
 
LAL83.3%
 
UTA83.3%
 
BKN83.3%
 
IND83.3%
 
CHI83.3%
 
MEM83.3%
 
CHA83.3%
 
ORL83.3%
 
ATL83.3%
 
PHI83.3%
 
BOS83.3%
 
SAS83.3%
 
POR83.3%
 
TOR83.3%
 
LAC83.3%
 
DET83.3%
 
HOU83.3%
 
SAC83.3%
 
GSW83.3%
 
Other values (5)4016.7%
 
2022-04-10T12:29:00.263663image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-04-10T12:29:00.315509image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters21
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
A8011.1%
 
L648.9%
 
O567.8%
 
I486.7%
 
N486.7%
 
C486.7%
 
S486.7%
 
M405.6%
 
H405.6%
 
T324.4%
 
E324.4%
 
P324.4%
 
D324.4%
 
R243.3%
 
K243.3%
 
B162.2%
 
U162.2%
 
W162.2%
 
X81.1%
 
Y81.1%
 
G81.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter720100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A8011.1%
 
L648.9%
 
O567.8%
 
I486.7%
 
N486.7%
 
C486.7%
 
S486.7%
 
M405.6%
 
H405.6%
 
T324.4%
 
E324.4%
 
P324.4%
 
D324.4%
 
R243.3%
 
K243.3%
 
B162.2%
 
U162.2%
 
W162.2%
 
X81.1%
 
Y81.1%
 
G81.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin720100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A8011.1%
 
L648.9%
 
O567.8%
 
I486.7%
 
N486.7%
 
C486.7%
 
S486.7%
 
M405.6%
 
H405.6%
 
T324.4%
 
E324.4%
 
P324.4%
 
D324.4%
 
R243.3%
 
K243.3%
 
B162.2%
 
U162.2%
 
W162.2%
 
X81.1%
 
Y81.1%
 
G81.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII720100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
A8011.1%
 
L648.9%
 
O567.8%
 
I486.7%
 
N486.7%
 
C486.7%
 
S486.7%
 
M405.6%
 
H405.6%
 
T324.4%
 
E324.4%
 
P324.4%
 
D324.4%
 
R243.3%
 
K243.3%
 
B162.2%
 
U162.2%
 
W162.2%
 
X81.1%
 
Y81.1%
 
G81.1%
 

GP
Real number (ℝ≥0)

Distinct43
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.80833333
Minimum27
Maximum80
Zeros0
Zeros (%)0.0%
Memory size2.0 KiB
2022-04-10T12:29:00.365774image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile44.9
Q159.75
median66
Q372
95-th percentile78
Maximum80
Range53
Interquartile range (IQR)12.25

Descriptive statistics

Standard deviation9.970077268
Coefficient of variation (CV)0.1538394332
Kurtosis0.9480414401
Mean64.80833333
Median Absolute Deviation (MAD)6
Skewness-0.9686777941
Sum15554
Variance99.40244073
MonotocityNot monotonic
2022-04-10T12:29:00.418387image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
64145.8%
 
65135.4%
 
69135.4%
 
73114.6%
 
66114.6%
 
63104.2%
 
75104.2%
 
74104.2%
 
72104.2%
 
67104.2%
 
7093.8%
 
5693.8%
 
6883.3%
 
7183.3%
 
5962.5%
 
6162.5%
 
7662.5%
 
7762.5%
 
7962.5%
 
5152.1%
 
7852.1%
 
6252.1%
 
6052.1%
 
8041.7%
 
5741.7%
 
Other values (18)3615.0%
 
ValueCountFrequency (%) 
2710.4%
 
3410.4%
 
3510.4%
 
3610.4%
 
4020.8%
 
4110.4%
 
4231.2%
 
4320.8%
 
4520.8%
 
4731.2%
 
ValueCountFrequency (%) 
8041.7%
 
7962.5%
 
7852.1%
 
7762.5%
 
7662.5%
 
75104.2%
 
74104.2%
 
73114.6%
 
72104.2%
 
7183.3%
 

MIN
Real number (ℝ≥0)

HIGH CORRELATION

Distinct228
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1802.225
Minimum924
Maximum2815
Zeros0
Zeros (%)0.0%
Memory size2.0 KiB
2022-04-10T12:29:00.473471image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum924
5-th percentile1087.95
Q11438.25
median1801
Q32130
95-th percentile2544.2
Maximum2815
Range1891
Interquartile range (IQR)691.75

Descriptive statistics

Standard deviation446.3004403
Coefficient of variation (CV)0.2476385803
Kurtosis-0.83218362
Mean1802.225
Median Absolute Deviation (MAD)341
Skewness0.05853181852
Sum432534
Variance199184.0831
MonotocityNot monotonic
2022-04-10T12:29:00.526842image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
229820.8%
 
156020.8%
 
139320.8%
 
122320.8%
 
120620.8%
 
158520.8%
 
208120.8%
 
150020.8%
 
198120.8%
 
140920.8%
 
101220.8%
 
180420.8%
 
191010.4%
 
139610.4%
 
236610.4%
 
186010.4%
 
186210.4%
 
254410.4%
 
237910.4%
 
186910.4%
 
187110.4%
 
136110.4%
 
187610.4%
 
136710.4%
 
136910.4%
 
Other values (203)20384.6%
 
ValueCountFrequency (%) 
92410.4%
 
94510.4%
 
96610.4%
 
97010.4%
 
98410.4%
 
101220.8%
 
105010.4%
 
105910.4%
 
106410.4%
 
107410.4%
 
ValueCountFrequency (%) 
281510.4%
 
278110.4%
 
271310.4%
 
270510.4%
 
267810.4%
 
265810.4%
 
261810.4%
 
259610.4%
 
259210.4%
 
258210.4%
 

POSS
Real number (ℝ≥0)

HIGH CORRELATION

Distinct233
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3738.479167
Minimum1894
Maximum5892
Zeros0
Zeros (%)0.0%
Memory size2.0 KiB
2022-04-10T12:29:00.586672image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1894
5-th percentile2275.9
Q13002.25
median3710.5
Q34444
95-th percentile5121.95
Maximum5892
Range3998
Interquartile range (IQR)1441.75

Descriptive statistics

Standard deviation917.1090058
Coefficient of variation (CV)0.245316067
Kurtosis-0.7978706498
Mean3738.479167
Median Absolute Deviation (MAD)722.5
Skewness0.05411490517
Sum897235
Variance841088.9284
MonotocityNot monotonic
2022-04-10T12:29:00.641078image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
415431.2%
 
384220.8%
 
420720.8%
 
490020.8%
 
261720.8%
 
251720.8%
 
512010.4%
 
394610.4%
 
344210.4%
 
431410.4%
 
291810.4%
 
291610.4%
 
342710.4%
 
444710.4%
 
495810.4%
 
461310.4%
 
444310.4%
 
446710.4%
 
549310.4%
 
446810.4%
 
341310.4%
 
344610.4%
 
344710.4%
 
387610.4%
 
345010.4%
 
Other values (208)20886.7%
 
ValueCountFrequency (%) 
189410.4%
 
193010.4%
 
198310.4%
 
199810.4%
 
203810.4%
 
211810.4%
 
212810.4%
 
218910.4%
 
221310.4%
 
223110.4%
 
ValueCountFrequency (%) 
589210.4%
 
586210.4%
 
573010.4%
 
566710.4%
 
552110.4%
 
551010.4%
 
549310.4%
 
540510.4%
 
536310.4%
 
518710.4%
 

%FGA
Real number (ℝ≥0)

HIGH CORRELATION

Distinct139
Distinct (%)57.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.3675
Minimum9.4
Maximum34.8
Zeros0
Zeros (%)0.0%
Memory size2.0 KiB
2022-04-10T12:29:00.695848image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum9.4
5-th percentile11.795
Q116.2
median20.05
Q323.825
95-th percentile30.205
Maximum34.8
Range25.4
Interquartile range (IQR)7.625

Descriptive statistics

Standard deviation5.45992877
Coefficient of variation (CV)0.2680706405
Kurtosis-0.3670674756
Mean20.3675
Median Absolute Deviation (MAD)3.85
Skewness0.2989150679
Sum4888.2
Variance29.81082218
MonotocityNot monotonic
2022-04-10T12:29:00.748591image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
19.962.5%
 
16.262.5%
 
23.152.1%
 
18.452.1%
 
18.552.1%
 
2541.7%
 
22.641.7%
 
17.641.7%
 
13.641.7%
 
21.241.7%
 
14.941.7%
 
15.631.2%
 
22.431.2%
 
24.931.2%
 
20.631.2%
 
15.531.2%
 
20.831.2%
 
23.431.2%
 
20.931.2%
 
15.731.2%
 
17.231.2%
 
26.831.2%
 
22.531.2%
 
19.631.2%
 
10.631.2%
 
Other values (114)14761.3%
 
ValueCountFrequency (%) 
9.410.4%
 
9.810.4%
 
10.210.4%
 
10.320.8%
 
10.510.4%
 
10.631.2%
 
11.210.4%
 
11.510.4%
 
11.710.4%
 
11.810.4%
 
ValueCountFrequency (%) 
34.810.4%
 
34.410.4%
 
33.710.4%
 
32.510.4%
 
32.210.4%
 
3210.4%
 
31.910.4%
 
31.110.4%
 
30.910.4%
 
30.810.4%
 

%3PA
Real number (ℝ≥0)

ZEROS

Distinct173
Distinct (%)72.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.51708333
Minimum0
Maximum41.1
Zeros3
Zeros (%)1.2%
Memory size2.0 KiB
2022-04-10T12:29:00.801325image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.49
Q114.575
median21.05
Q327.85
95-th percentile34.715
Maximum41.1
Range41.1
Interquartile range (IQR)13.275

Descriptive statistics

Standard deviation9.751798452
Coefficient of variation (CV)0.475301401
Kurtosis-0.3846892111
Mean20.51708333
Median Absolute Deviation (MAD)6.7
Skewness-0.4144287877
Sum4924.1
Variance95.09757305
MonotocityNot monotonic
2022-04-10T12:29:00.849496image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
0.172.9%
 
26.252.1%
 
21.241.7%
 
031.2%
 
17.531.2%
 
2831.2%
 
28.431.2%
 
22.931.2%
 
20.131.2%
 
31.631.2%
 
23.831.2%
 
22.831.2%
 
3031.2%
 
11.331.2%
 
25.731.2%
 
2131.2%
 
3120.8%
 
21.720.8%
 
2020.8%
 
17.720.8%
 
20.320.8%
 
17.920.8%
 
16.220.8%
 
1120.8%
 
31.220.8%
 
Other values (148)16769.6%
 
ValueCountFrequency (%) 
031.2%
 
0.172.9%
 
0.210.4%
 
0.310.4%
 
0.510.4%
 
0.710.4%
 
0.810.4%
 
0.910.4%
 
1.610.4%
 
1.710.4%
 
ValueCountFrequency (%) 
41.110.4%
 
39.810.4%
 
39.410.4%
 
37.910.4%
 
37.510.4%
 
37.110.4%
 
3710.4%
 
35.810.4%
 
35.410.4%
 
35.210.4%
 

%FTA
Real number (ℝ≥0)

HIGH CORRELATION

Distinct173
Distinct (%)72.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.2
Minimum2.2
Maximum62.8
Zeros0
Zeros (%)0.0%
Memory size2.0 KiB
2022-04-10T12:29:00.899766image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile6.6
Q112.9
median18.75
Q325.025
95-th percentile40.08
Maximum62.8
Range60.6
Interquartile range (IQR)12.125

Descriptive statistics

Standard deviation10.52114428
Coefficient of variation (CV)0.5208487268
Kurtosis1.395457794
Mean20.2
Median Absolute Deviation (MAD)6.2
Skewness1.022263537
Sum4848
Variance110.694477
MonotocityNot monotonic
2022-04-10T12:29:00.947017image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
19.341.7%
 
16.641.7%
 
23.941.7%
 
21.541.7%
 
17.331.2%
 
2331.2%
 
6.631.2%
 
11.931.2%
 
23.831.2%
 
12.131.2%
 
15.131.2%
 
21.631.2%
 
17.720.8%
 
17.920.8%
 
10.420.8%
 
4.420.8%
 
15.320.8%
 
2620.8%
 
20.420.8%
 
11.120.8%
 
16.520.8%
 
12.920.8%
 
31.220.8%
 
17.620.8%
 
6.920.8%
 
Other values (148)17472.5%
 
ValueCountFrequency (%) 
2.210.4%
 
3.210.4%
 
4.420.8%
 
4.810.4%
 
5.110.4%
 
5.410.4%
 
5.610.4%
 
5.710.4%
 
610.4%
 
6.110.4%
 
ValueCountFrequency (%) 
62.810.4%
 
61.110.4%
 
46.710.4%
 
46.610.4%
 
45.710.4%
 
45.510.4%
 
45.210.4%
 
43.710.4%
 
43.510.4%
 
42.910.4%
 

%REB
Real number (ℝ≥0)

Distinct170
Distinct (%)70.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.87333333
Minimum7.4
Maximum51.4
Zeros0
Zeros (%)0.0%
Memory size2.0 KiB
2022-04-10T12:29:00.999792image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum7.4
5-th percentile9.395
Q113.2
median16.7
Q325.525
95-th percentile37.42
Maximum51.4
Range44
Interquartile range (IQR)12.325

Descriptive statistics

Standard deviation8.88693492
Coefficient of variation (CV)0.4471788789
Kurtosis0.4094575296
Mean19.87333333
Median Absolute Deviation (MAD)4.7
Skewness1.01795768
Sum4769.6
Variance78.97761227
MonotocityNot monotonic
2022-04-10T12:29:01.049886image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
13.362.5%
 
14.552.1%
 
12.752.1%
 
12.852.1%
 
12.652.1%
 
1441.7%
 
13.931.2%
 
13.231.2%
 
12.331.2%
 
14.431.2%
 
15.731.2%
 
35.520.8%
 
12.420.8%
 
21.420.8%
 
15.920.8%
 
23.420.8%
 
10.920.8%
 
21.920.8%
 
17.720.8%
 
7.920.8%
 
13.120.8%
 
15.220.8%
 
17.620.8%
 
11.520.8%
 
14.820.8%
 
Other values (145)16769.6%
 
ValueCountFrequency (%) 
7.410.4%
 
7.710.4%
 
7.920.8%
 
8.510.4%
 
8.610.4%
 
8.920.8%
 
910.4%
 
9.120.8%
 
9.310.4%
 
9.410.4%
 
ValueCountFrequency (%) 
51.410.4%
 
46.310.4%
 
44.610.4%
 
43.410.4%
 
43.310.4%
 
41.710.4%
 
4010.4%
 
39.710.4%
 
39.310.4%
 
38.210.4%
 

%AST
Real number (ℝ≥0)

Distinct175
Distinct (%)72.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.9825
Minimum2.9
Maximum55.5
Zeros0
Zeros (%)0.0%
Memory size2.0 KiB
2022-04-10T12:29:01.098716image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile7.6
Q111.275
median16.85
Q327.85
95-th percentile40.415
Maximum55.5
Range52.6
Interquartile range (IQR)16.575

Descriptive statistics

Standard deviation10.90651153
Coefficient of variation (CV)0.5458031542
Kurtosis0.2217206478
Mean19.9825
Median Absolute Deviation (MAD)6.65
Skewness0.9189334814
Sum4795.8
Variance118.9519937
MonotocityNot monotonic
2022-04-10T12:29:01.152046image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
14.841.7%
 
9.331.2%
 
32.831.2%
 
32.331.2%
 
9.531.2%
 
16.831.2%
 
13.731.2%
 
11.231.2%
 
1231.2%
 
11.631.2%
 
24.131.2%
 
10.931.2%
 
20.331.2%
 
9.231.2%
 
10.831.2%
 
14.231.2%
 
21.431.2%
 
20.531.2%
 
15.520.8%
 
11.920.8%
 
7.620.8%
 
11.320.8%
 
17.820.8%
 
12.820.8%
 
33.720.8%
 
Other values (150)17171.2%
 
ValueCountFrequency (%) 
2.910.4%
 
4.510.4%
 
4.720.8%
 
6.320.8%
 
6.910.4%
 
710.4%
 
7.110.4%
 
7.210.4%
 
7.510.4%
 
7.620.8%
 
ValueCountFrequency (%) 
55.510.4%
 
53.310.4%
 
52.710.4%
 
51.510.4%
 
45.310.4%
 
44.910.4%
 
44.710.4%
 
43.510.4%
 
4310.4%
 
41.810.4%
 

%BLKA
Real number (ℝ≥0)

Distinct155
Distinct (%)64.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.70083333
Minimum4.7
Maximum45.5
Zeros0
Zeros (%)0.0%
Memory size2.0 KiB
2022-04-10T12:29:01.202389image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum4.7
5-th percentile8.4
Q113.975
median19.35
Q323.925
95-th percentile34.2
Maximum45.5
Range40.8
Interquartile range (IQR)9.95

Descriptive statistics

Standard deviation7.731298081
Coefficient of variation (CV)0.3924350788
Kurtosis0.220598653
Mean19.70083333
Median Absolute Deviation (MAD)5.25
Skewness0.5673201474
Sum4728.2
Variance59.77297001
MonotocityNot monotonic
2022-04-10T12:29:01.251966image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
21.952.1%
 
14.952.1%
 
23.652.1%
 
20.141.7%
 
10.641.7%
 
19.441.7%
 
27.441.7%
 
24.641.7%
 
11.631.2%
 
15.431.2%
 
19.231.2%
 
13.131.2%
 
21.831.2%
 
13.431.2%
 
1731.2%
 
18.631.2%
 
1431.2%
 
12.531.2%
 
13.620.8%
 
2420.8%
 
20.820.8%
 
12.820.8%
 
21.120.8%
 
26.520.8%
 
25.420.8%
 
Other values (130)16167.1%
 
ValueCountFrequency (%) 
4.710.4%
 
5.310.4%
 
5.610.4%
 
5.910.4%
 
6.610.4%
 
7.110.4%
 
7.310.4%
 
7.520.8%
 
8.210.4%
 
8.310.4%
 
ValueCountFrequency (%) 
45.510.4%
 
42.610.4%
 
41.810.4%
 
38.310.4%
 
37.910.4%
 
37.710.4%
 
37.110.4%
 
36.410.4%
 
35.520.8%
 
34.910.4%
 

%PFD
Real number (ℝ≥0)

HIGH CORRELATION

Distinct174
Distinct (%)72.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.08458333
Minimum3
Maximum57.9
Zeros0
Zeros (%)0.0%
Memory size2.0 KiB
2022-04-10T12:29:01.304676image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile7.68
Q113.275
median18.5
Q325.1
95-th percentile37.805
Maximum57.9
Range54.9
Interquartile range (IQR)11.825

Descriptive statistics

Standard deviation9.383278316
Coefficient of variation (CV)0.4671880995
Kurtosis1.327252158
Mean20.08458333
Median Absolute Deviation (MAD)5.9
Skewness0.9667719919
Sum4820.3
Variance88.04591196
MonotocityNot monotonic
2022-04-10T12:29:01.350459image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
15.841.7%
 
8.941.7%
 
18.541.7%
 
11.841.7%
 
13.931.2%
 
24.431.2%
 
16.431.2%
 
11.531.2%
 
9.831.2%
 
28.620.8%
 
25.120.8%
 
8.720.8%
 
16.520.8%
 
1520.8%
 
16.720.8%
 
21.220.8%
 
18.720.8%
 
21.720.8%
 
15.920.8%
 
32.220.8%
 
15.220.8%
 
17.920.8%
 
24.120.8%
 
20.620.8%
 
2420.8%
 
Other values (149)17773.8%
 
ValueCountFrequency (%) 
310.4%
 
4.510.4%
 
4.910.4%
 
610.4%
 
6.310.4%
 
6.420.8%
 
6.610.4%
 
6.710.4%
 
7.110.4%
 
7.210.4%
 
ValueCountFrequency (%) 
57.910.4%
 
57.810.4%
 
45.910.4%
 
41.320.8%
 
41.210.4%
 
4110.4%
 
40.210.4%
 
39.410.4%
 
39.110.4%
 
3810.4%
 

%PTS
Real number (ℝ≥0)

HIGH CORRELATION

Distinct140
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.45166667
Minimum10.1
Maximum38.7
Zeros0
Zeros (%)0.0%
Memory size2.0 KiB
2022-04-10T12:29:01.400996image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum10.1
5-th percentile11.99
Q116.475
median19.95
Q323.7
95-th percentile31.305
Maximum38.7
Range28.6
Interquartile range (IQR)7.225

Descriptive statistics

Standard deviation5.573217235
Coefficient of variation (CV)0.272506751
Kurtosis0.2609328524
Mean20.45166667
Median Absolute Deviation (MAD)3.65
Skewness0.5719561624
Sum4908.4
Variance31.06075035
MonotocityNot monotonic
2022-04-10T12:29:01.450734image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
19.662.5%
 
1952.1%
 
19.541.7%
 
21.441.7%
 
21.341.7%
 
16.241.7%
 
20.941.7%
 
16.641.7%
 
22.541.7%
 
18.831.2%
 
18.631.2%
 
19.731.2%
 
22.431.2%
 
16.131.2%
 
12.731.2%
 
17.731.2%
 
17.931.2%
 
2031.2%
 
23.231.2%
 
26.231.2%
 
20.231.2%
 
13.831.2%
 
22.331.2%
 
1531.2%
 
23.931.2%
 
Other values (115)15363.7%
 
ValueCountFrequency (%) 
10.110.4%
 
10.210.4%
 
10.510.4%
 
10.920.8%
 
1120.8%
 
11.310.4%
 
11.410.4%
 
11.610.4%
 
11.710.4%
 
11.810.4%
 
ValueCountFrequency (%) 
38.710.4%
 
36.410.4%
 
35.210.4%
 
34.910.4%
 
3410.4%
 
33.310.4%
 
33.210.4%
 
33.110.4%
 
32.810.4%
 
31.720.8%
 

Interactions

2022-04-10T12:28:51.944987image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:52.001194image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:52.055462image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:52.111328image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:52.168483image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:52.220558image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:52.271595image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:52.324883image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:52.376146image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:52.428046image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:52.486124image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:52.538773image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:52.592147image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:52.642700image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:52.691311image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:52.742755image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:52.795275image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:52.843725image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:52.890843image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:52.940163image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:52.987607image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:53.036237image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:53.088498image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:53.138430image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:53.188123image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:53.242509image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:53.294593image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:53.351119image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:53.408742image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:53.461265image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:53.512251image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:53.568198image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:53.621473image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:53.672795image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:53.728636image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:53.781276image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:54.056805image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:54.113953image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:54.168028image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:54.225141image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:54.283181image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:54.337170image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:54.389968image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:54.445171image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:54.498491image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:54.551617image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:54.609245image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:54.663521image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:54.721116image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:54.772227image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:54.820856image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:54.872727image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:54.925592image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:54.974161image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:55.022688image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:55.073331image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:55.121721image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:55.169359image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:55.222958image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:55.272103image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:55.321868image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:55.370451image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:55.416887image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-04-10T12:28:55.466279image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-10T12:29:01.597489image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-10T12:29:01.688955image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-10T12:29:01.779189image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-04-10T12:29:01.858711image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.